Digital

The interdisciplinary teaching of contemporary physics has become increasingly common in physics education, especially for high school students and teachers. This approach, which integrates content and methodologies from various disciplines, fosters scientific reasoning, enhances creativity, and increases student motivation and interest in physics. The use of digital technologies, such as social media platforms, supports these educational goals by facilitating the inclusive and cost-effective dissemination of scientific knowledge and the development of soft skills. This paper introduces the “Gravitas” project, an initiative that employs an interdisciplinary approach to present contemporary physics topics to high school students through social media. Coordinated by the Cagliari Division of the National Institute of Nuclear Physics (INFN) in Italy, the “Gravitas” project offers a non-traditional learning environment where students explore modern physics and philosophy and the history of science. Through the creation of educational materials, such as social media posts, students actively engage in their learning. In 2022, around 250 students from 16 high schools across Sardinia, Italy, participated in this project. This paper discusses the learning outcomes, highlighting the potential of integrating formal high school curricula with innovative educational and digital tools. Full article

As the digital environment evolves, the need to integrate artificial intelligence (AI) technology into corporate IT network operations increases. In this study, the aim was to define the factors that influence AI adoption in the network operations and analyze their impact on productivity and service stability. The technology–organization–environment (TOE) framework was employed for this investigation, focusing on technological, organizational, and environmental factors. In addition, in this study, structural equation modeling was employed to analyze the relationships between these influencing factors and the intention to adopt AI. The mediation effect was examined through the network operation productivity and network service stability. A survey was conducted targeting network operations and AI professionals to collect data. The analysis results revealed that technological and environmental factors positively influenced the network operation productivity, while only environmental factors positively influenced the network service stability. Furthermore, the findings of this study highlight that environmental factors are the most significant factors that influence network operation productivity and network service stability. Moreover, the direct positive impact of network operation productivity and IT network service stability on the intention to adopt AI underscores their crucial role. In conclusion, when evaluating AI adoption in terms of network operation productivity and network service stability, prioritizing technological and environmental factors over organizational factors is necessary. Full article

Three-dimensional information visualizations are widely used in various fields for their aesthetic appeal. However, using them can sometimes lead to occlusion and distortion, which raises questions about when and why to use them. In this study, we aimed to investigate the effects of three-dimensional visualizations on human gaze and user understanding and analyze the perception process in detail. Our empirical research consisted of a two-part experimental study that involved both subjective and objective evaluation. We specifically focused on bar charts as they are among the most frequently used types of information visualizations. The results suggest that, for bar chart visualizations with varying gap dimensions, there is no statistically significant difference in user understanding between the two-dimensional and three-dimensional versions. Our findings indicate that, in general, three-dimensional bar chart visualizations are as comprehensible as their two-dimensional counterparts for the gap dimensions examined in this research. This study provides empirical insights demonstrating that both 3D and 2D bar charts are equally understandable, particularly when a specific gap depth is used in 3D visualizations. These findings contribute to the ongoing discussion about the effective use of three-dimensional visualizations and highlight areas for further research. Full article

This pilot study explores the effectiveness of digital storytelling in improving the oral use of the passive voice among Lebanese undergraduate EFL students. Conducted during the 2021/2022 spring semester amidst Lebanon’s ongoing economic and social crises, the study involved an experimental group using a digital storytelling strategy and a control group receiving traditional instruction. The research employed a quantitative approach, utilizing a pretest and a posttest to assess grammatical accuracy and fluency, and qualitative interviews to gauge student perceptions. The findings indicate that digital storytelling significantly enhances students’ ability to use the passive voice in oral communication, fostering greater engagement and a deeper understanding of grammatical structures. Despite the challenges posed by the COVID-19 pandemic and Lebanon’s economic difficulties, students in the experimental group demonstrated marked improvement over those in the control group. The study’s limitations include its small sample size and the specific context of a private Lebanese university, which may limit generalizability. However, the results offer promising insights into the benefits of digital storytelling as a pedagogical tool, suggesting its potential for broader application in EFL education. This research contributes to the growing body of literature on technology-enhanced language learning and underscores the need for further exploration in diverse educational settings. Full article

Accurate and efficient estrus detection in sows is crucial in modern agricultural practices to ensure optimal reproductive health and successful breeding outcomes. A non-contact method using computer vision to detect a change in a sow’s vulva size holds great promise for automating and enhancing this critical process. However, achieving precise and reliable results depends heavily on maintaining a consistent camera distance during image capture. Variations in camera distance can lead to erroneous estrus estimations, potentially resulting in missed breeding opportunities or false positives. To address this challenge, we propose a robust six-step methodology, accompanied by three stages of evaluation. First, we carefully annotated masks around the vulva to ensure an accurate pixel perimeter calculation of its shape. Next, we meticulously identified keypoints on the sow’s vulva, which enabled precise tracking and analysis of its features. We then harnessed the power of machine learning to train our model using annotated images, which facilitated keypoint detection and segmentation with the state-of-the-art YOLOv8 algorithm. By identifying the keypoints, we performed precise calculations of the Euclidean distances: first, between each labium (horizontal distance), and second, between the clitoris and the perineum (vertical distance). Additionally, by segmenting the vulva’s size, we gained valuable insights into its shape, which helped with performing precise perimeter measurements. Equally important was our effort to calibrate the camera using monocular depth estimation. This calibration helped establish a functional relationship between the measurements on the image (such as the distances between the labia and from the clitoris to the perineum, and the vulva perimeter) and the depth distance to the camera, which enabled accurate adjustments and calibration for our analysis. Lastly, we present a classification method for distinguishing between estrus and non-estrus states in subjects based on the pixel width, pixel length, and perimeter measurements. The method calculated the Euclidean distances between a new data point and reference points from two datasets: “estrus data” and “not estrus data”. Using custom distance functions, we computed the distances for each measurement dimension and aggregated them to determine the overall similarity. The classification process involved identifying the three nearest neighbors of the datasets and employing a majority voting mechanism to assign a label. A new data point was classified as “estrus” if the majority of the nearest neighbors were labeled as estrus; otherwise, it was classified as “non-estrus”. This method provided a robust approach for automated classification, which aided in more accurate and efficient detection of the estrus states. To validate our approach, we propose three evaluation stages. In the first stage, we calculated the Mean Squared Error (MSE) between the ground truth keypoints of the labia distance and the distance between the predicted keypoints, and we performed the same calculation for the distance between the clitoris and perineum. Then, we provided a quantitative analysis and performance comparison, including a comparison between our previous U-Net model and our new YOLOv8 segmentation model. This comparison focused on each model’s performance in terms of accuracy and speed, which highlighted the advantages of our new approach. Lastly, we evaluated the estrus–not-estrus classification model by defining the confusion matrix. By using this comprehensive approach, we significantly enhanced the accuracy of estrus detection in sows while effectively mitigating human errors and resource wastage. The automation and optimization of this critical process hold the potential to revolutionize estrus detection in agriculture, which will contribute to improved reproductive health management and elevate breeding outcomes to new heights. Through extensive evaluation and experimentation, our research aimed to demonstrate the transformative capabilities of computer vision techniques, paving the way for more advanced and efficient practices in the agricultural domain. Full article

In response to the escalating threats of cybersecurity attacks and breaches, ensuring the development and deployment of secure IT products has become paramount for organizations in their cybersecurity transformation. This work emphasizes the critical need for a comprehensive and secure IT project management life cycle that safeguards products from their initial development stages through decommissioning. The primary objective is to seamlessly integrate security considerations into every facet of IT project management life cycles. This work embraces a cyber-resilient IT project management framework and advocates the inclusion of cybersecurity measures in IT projects and their strategic, organized, continuous, and systematic integration throughout the entire product life cycle. It introduces a pioneering framework that harmonizes the cybersecurity risk management process with the IT project management life cycle. This framework delineates a methodical sequence of steps, each encompassing a distinct set of activities. The effectiveness and practical applicability of the proposed framework were validated through a comprehensive case study focused on the Personal Health Record (PHR) system. The PHR case study served as a real-world scenario to assess the framework’s ability to address cybersecurity challenges in a specific domain. The results of the experiment demonstrated the framework’s efficacy in enhancing the security posture of IT projects, showcasing its adaptability and scalability across diverse applications. Full article

Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN. Full article

Urban mobility systems generate a massive volume of real-time data, providing an exceptional opportunity to understand and optimize transportation networks. To harness this potential, we developed UrbanFlow Milano, an interactive map-based dashboard designed to explore the intricate patterns of shared mobility use within the city of Milan. By placing users at the center of the analysis, UrbanFlow empowers them to visualize, filter, and interact with data to uncover valuable insights. Through a comprehensive user study, we observed how individuals interact with the dashboard, gaining critical feedback to refine its design and enhance its effectiveness. Our research contributes to the advancement of user-centric visual analytics tools that facilitate data-driven decision-making in urban planning and transportation management. Full article

The authors focus on innovating the research tool Urban’s Test of Creativity to enhance the evaluation and efficiency of the educational process. This paper presents the possibility of measuring creativity and spatial imagination in the SketchUp virtual environment. Teachers and HR professionals in modern companies require an overview of the key competencies of students and graduates, essential for the transformation towards Industry 5.0. The authors utilize the proven concept of Urban’s Test, modify it into a digital format, and integrate new elements that assess spatial visualization and functional creativity. Teachers and HR professionals gain an efficient tool that is easy to evaluate, time-efficient, and requires minimal infrastructure. The modified research tool is suitable for conducting action research and allows for comparing respondents’ results using quantitative methods. This pilot study aimed to validate the modified test and its properties. A total of one hundred respondents, divided into five groups based on age and education (approximately 10 to 25 years old), were tested. The results confirmed that the concept of Urban’s Test of Creativity can be successfully adapted for commonly available 3D modeling environments. Moreover, it was confirmed that within the target group, the level of spatial visualization improves gradually with age. Full article

Blockchain technology (BCT) is regarded as one of the most important and disruptive technologies in Industry 4.0. However, no comprehensive study addresses the contributions of BCT adoption (BCA) on some special business functionalities projected as financial variables like BCA integrity, transparency, etc. Therefore, the primary objective of this study was to close this theoretical gap and determine how BCA has contributed to the four business sectors that were selected since FinTech had the greatest potential in these domains. The PRISMA approach, a systematic literature review model, was used in this work to make sure that the greatest number of studies on the topic were accessed. The PRISMA model’s output helped identify relevant publications, and an analysis of these studies served as the foundation for this paper’s findings. The findings reveal that BCA for companies with a disrupting financial technology (FinTech) attitude can help in securing corporate transaction transparency; offer knowledge, same-data, and information sharing; enhance fidelity, integrity, and trust; improve organizational procedures; and prevent fraud with cyber-hacking protection and fraudulence suspension. Moreover, blockchain’s smart contract utilization feature offers ESG and sustainability functionality. This paper’s novelty is the projection to four business sectors of the three-layer research sequence: (i) financial variables operated as BCA functionalities, (ii) issues, risks, limitations, and opportunities associated with the financial variables, and (iii) implications, theoretical contributions, questions, potentiality, and outlook of BCA/FinTech issues. And the ability of managers or practitioners to reference this sequence and make decisions on BCA matters is considered a key contribution. The proposed methodology provides business practitioners with valuable insights to reevaluate their economic challenges and explore the potential of blockchain technology to address them. This study combined a systematic literature review (SLR) with qualitative analysis as part of a hybrid research approach. Quantitative analysis was carried out on all 835 selected papers in the first step, and qualitative analysis was carried out on the top-cited papers that were screened. The current work highlights the key challenges and opportunities in established blockchain implementations and discusses the outlook potentiality of blockchain technology adoption. This study will be useful to managers, practitioners, researchers, and scholars. Full article

This systematic literature review aimed to assess the methodological quality of user-centered usability evaluation of digital applications to promote citizens’ engagement and participation in public governance by (i) systematizing their purposes; (ii) analyzing the evaluation procedures, methods, and instruments that were used; (iii) determining their conformance with recommended usability evaluation good practices; and (iv) identifying the implications of the reported results for future developments. An electronic search was conducted on Web of Science, Scopus, and IEEE Xplore databases, and after a screening procedure considering predefined eligibility criteria, 34 studies were reviewed. These studies performed user-centered usability evaluation of digital applications related to (i) participatory reporting of urban issues, (ii) environmental sustainability, (iii) civic participation, (iv) urban planning, (v) promotion of democratic values, (vi) electronic voting, and (vii) chatbots. In terms of the methodological quality of the included studies, the results suggest that there is a high heterogeneity of the user-centered usability evaluation. Therefore, there is a need for recommendations to support user-centered usability evaluations of digital applications to promote citizens’ engagement and participation in public governance to improve the planning and conduction of future research. Full article

Pursuing human-centered, sustainable, and resilient production is shaping a future-oriented approach to manufacturing processes in the context of Industry 5.0. How can such production be implemented? For this purpose, this article analyses the effects of the developed Dresden Model of Adaptability (acronym: DreMoWabe) on the integration of holistic sustainability. The focus is on investigating the promotion of economic, environmental, and social sustainability goals in terms of the 17 Sustainable Development Goals and analyzing strategies to increase resilience to changing environmental conditions. A human-centered perspective is considered. The model proves to be a holistic approach that drives sustainable development of the production system through the comprehensive integration of human, technology, and organizational structures. Full article

This study aims to characterize the digital skills and pedagogical practices using digital technology among primary teachers (first CEB), as well as to map their understanding and practices of promoting gender equality using digital technology. To this end, an online questionnaire survey was conducted among teachers, which yielded 3871 valid responses, representing 17.5% of the population. The sample structure is identical to that of the population, in terms of sociodemographic and territorial characteristics, as well as to the years of schooling covered. It was found that teachers perceive themselves as digitally competent to carry out the essential part of their teaching tasks. Most have already tried to carry out a diverse set of pedagogical practices and activities using digital technology. However, as the overwhelming majority did not find differences between the uses that boys and girls make of digital technology, they also do not act to promote digital skills differently among girls. Some contextual variables were also found that explain these differences in perception and practices regarding digital in education. Full article

The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis (SA) as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of this study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, the results highlight the necessity of developing specialized lexicons tailored not only to a specific language but also to a particular field, such as a specific type of restaurant or shop. Full article

Identifying and understanding visitor needs and expectations is of the utmost importance for a number of stakeholders and policymakers involved in the touristic domain. Apart from traditional forms of feedback, an abundance of related information exists online, scattered across various data sources like online social media, tourism-related platforms, traveling blogs, forums, etc. Retrieving and analyzing the aforementioned content is not a straightforward task and in order to address this challenge, we have developed the StreetLines platform, a novel information system that is able to collect, analyze and produce insights from the available tourism-related data. Its highly modular architecture allows for the continuous monitoring of varying pools of heterogeneous data sources whose contents are subsequently stored, after preprocessing, in a data repository. Following that, the aforementioned data feed a number of independent and parallel processing modules that extract useful information for all individuals involved in the tourism domain, like place recommendation for visitors and sentiment analysis and keyword extraction reports for professionals in the tourism industry. The presented platform is an outcome of the StreetLines project and apart from the contributions of its individual components, its novelty lies in the holistic approach to knowledge extraction and tourism data mining. Full article

The study empirically analyzed activism participants’ roles drawn from the lens of social media affordance and identified the activism opinion leaders based on the framework of network connectivity, message diffusion, and semantic relevancy through the case of the #Marchforourlives Twitter network, which has been rebranded as X. The study defines the #Marchforourlives Twitter network as a co-created activism network in collaboration with different degrees of contributors, such as the core advocates, the advocates, the supporters, and the amplifiers. The results showed that a very small number of tweets created by the core advocates played significant roles due to their extensive adoption by other participants, while many other original tweets were never mentioned or retweeted in the network. This study disclosed the extensive proportion of amplifiers as 95.13% among the examined participants. The study findings suggest that creating core agenda tweets with high amplifiability might be critical for successful hashtag activism to attract like-minded masses as networked protesters. Full article

ChatGPT has significantly increased in popularity in recent months because of its capacity to generate novel content and provide genuine responses to questions. Nevertheless, like all technologies, it is crucial to assess its limitations and features prior to implementing it into an educational setting. A major obstacle associated with ChatGPT is its tendency to produce consistent yet occasionally unreliable and inaccurate responses. Our study provides students with training in this area, and its objective was to analyse the opinion of those same university students studying education-related degrees regarding the efficacy of the usefulness of ChatGPT for their learning. We used a mixed methodology and two instruments for data collection: questionnaires and discussion groups. The sample comprised 150 university students pursuing degrees in teaching and social education. The results show that the majority of students are familiar with the technology but have not had any formal training in a university. They use this tool to complete academic assignments outside the classroom, and they emphasise the need for training in it. Furthermore, following the training, the students highlight an increase in motivation and a positive impact on the development of generic skills, such as information analysis, synthesis and management, problem solving, and learning how to learn. Ultimately, this study provides an opportunity to consider the implementation of educational training of this tool at the university level in order to ensure its appropriate use. Full article

Animated videos have been used in education for many years, and their efficacy in enhancing student motivation, engagement, and performance has been evaluated and reported in many studies. The aim of this study is twofold. First, after examining seventy-seven research articles, this study will attempt to provide an updated comprehensive literature review on the topic for the last decade. The articles were obtained from Google Scholar and Scopus following a certain methodology (search keywords, inclusion and exclusion criteria). The articles were examined for aspects such as the educational fields in which animated videos have been utilized over the last ten years, the researchers’ countries, the types of animated videos, the software tools used to create the educational animations, the research methods employed, and the aims and findings of the studies. The second part of this paper will present animated videos produced by teachers together with their experiences from the development process and classroom use. This study concentrates on the software tools the educators chose to use and their perceptions about developing their own animations. Findings indicate that when animated videos are produced by teachers, their creativity is boosted, and their communication skills are enhanced. Full article

By exploring the top three airports in Asia, this study explores the area of smart airport systems. With the goal of analyzing the significant elements of airport services that captivate travelers’ attention through online reviews and establishing a correlation between sentiment in reviews and numerical ratings given by travelers, the study analyzes what captivates travelers’ attention. Data mining, frequency analysis, sentiment analysis, and linear regression are employed in this study in order to analyze a dataset of 10,202 online reviews. The results indicate that the most common attributes of airport services significantly impact customer satisfaction, as well as how the sentiment expressed in online reviews correlates with the numerical ratings. A significant contribution of this study lies in its contribution to understanding the dynamics of customer satisfaction in the field of airport services as well as in identifying areas for improvement that could enhance the overall traveler experience in the burgeoning field of smart airports. In the context of smart airport systems, the analysis of exploratory and predictive data provides valuable insights into the optimization of airport operations, thus enriching the body of knowledge in this rapidly evolving area and providing the foundation for future research. Full article